Overview

Dataset statistics

Number of variables15
Number of observations63222
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 MiB
Average record size in memory120.0 B

Variable types

Numeric11
Categorical4

Alerts

qrs_end is highly overall correlated with qrs_onset and 1 other fieldsHigh correlation
qrs_onset is highly overall correlated with qrs_end and 1 other fieldsHigh correlation
rr_interval is highly overall correlated with t_endHigh correlation
t_end is highly overall correlated with qrs_end and 2 other fieldsHigh correlation
bandwidth is highly imbalanced (73.8%)Imbalance
filtering is highly imbalanced (90.4%)Imbalance
p_onset is highly skewed (γ1 = -138.0943931)Skewed
qrs_axis has 1390 (2.2%) zerosZeros

Reproduction

Analysis started2024-01-21 23:48:49.937476
Analysis finished2024-01-21 23:49:23.700551
Duration33.76 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

anchor_age
Real number (ℝ)

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.722233
Minimum18
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:23.829390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile32
Q151
median61
Q372
95-th percentile85
Maximum91
Range73
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.459239
Coefficient of variation (CV)0.25458944
Kurtosis-0.23792036
Mean60.722233
Median Absolute Deviation (MAD)10
Skewness-0.32368637
Sum3838981
Variance238.98808
MonotonicityNot monotonic
2024-01-22T05:19:24.033215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 1836
 
2.9%
61 1779
 
2.8%
60 1735
 
2.7%
66 1696
 
2.7%
62 1685
 
2.7%
59 1647
 
2.6%
56 1547
 
2.4%
67 1524
 
2.4%
54 1511
 
2.4%
65 1511
 
2.4%
Other values (63) 46751
73.9%
ValueCountFrequency (%)
18 43
 
0.1%
19 99
 
0.2%
20 177
0.3%
21 174
0.3%
22 213
0.3%
23 237
0.4%
24 225
0.4%
25 250
0.4%
26 215
0.3%
27 275
0.4%
ValueCountFrequency (%)
91 1094
1.7%
89 245
 
0.4%
88 421
 
0.7%
87 370
 
0.6%
86 654
1.0%
85 612
1.0%
84 687
1.1%
83 840
1.3%
82 831
1.3%
81 844
1.3%

bandwidth
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size494.0 KiB
0.005-150 Hz
58576 
0.05-150 Hz
 
4125
0.0005-150 Hz
 
521

Length

Max length13
Median length12
Mean length11.942995
Min length11

Characters and Unicode

Total characters755060
Distinct characters8
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.005-150 Hz
2nd row0.005-150 Hz
3rd row0.005-150 Hz
4th row0.005-150 Hz
5th row0.005-150 Hz

Common Values

ValueCountFrequency (%)
0.005-150 Hz 58576
92.7%
0.05-150 Hz 4125
 
6.5%
0.0005-150 Hz 521
 
0.8%

Length

2024-01-22T05:19:24.207778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T05:19:24.377661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
hz 63222
50.0%
0.005-150 58576
46.3%
0.05-150 4125
 
3.3%
0.0005-150 521
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 249284
33.0%
5 126444
16.7%
. 63222
 
8.4%
- 63222
 
8.4%
1 63222
 
8.4%
63222
 
8.4%
H 63222
 
8.4%
z 63222
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 438950
58.1%
Other Punctuation 63222
 
8.4%
Dash Punctuation 63222
 
8.4%
Space Separator 63222
 
8.4%
Uppercase Letter 63222
 
8.4%
Lowercase Letter 63222
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249284
56.8%
5 126444
28.8%
1 63222
 
14.4%
Other Punctuation
ValueCountFrequency (%)
. 63222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63222
100.0%
Space Separator
ValueCountFrequency (%)
63222
100.0%
Uppercase Letter
ValueCountFrequency (%)
H 63222
100.0%
Lowercase Letter
ValueCountFrequency (%)
z 63222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 628616
83.3%
Latin 126444
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249284
39.7%
5 126444
20.1%
. 63222
 
10.1%
- 63222
 
10.1%
1 63222
 
10.1%
63222
 
10.1%
Latin
ValueCountFrequency (%)
H 63222
50.0%
z 63222
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 755060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249284
33.0%
5 126444
16.7%
. 63222
 
8.4%
- 63222
 
8.4%
1 63222
 
8.4%
63222
 
8.4%
H 63222
 
8.4%
z 63222
 
8.4%

filtering
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size494.0 KiB
60 Hz notch Baseline filter
61990 
Baseline filter
 
1008
50 Hz notch Baseline filter
 
224

Length

Max length27
Median length27
Mean length26.808674
Min length15

Characters and Unicode

Total characters1694898
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60 Hz notch Baseline filter
2nd row60 Hz notch Baseline filter
3rd row60 Hz notch Baseline filter
4th row60 Hz notch Baseline filter
5th row60 Hz notch Baseline filter

Common Values

ValueCountFrequency (%)
60 Hz notch Baseline filter 61990
98.1%
Baseline filter 1008
 
1.6%
50 Hz notch Baseline filter 224
 
0.4%

Length

2024-01-22T05:19:24.485337image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T05:19:24.630888image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
baseline 63222
20.2%
filter 63222
20.2%
hz 62214
19.9%
notch 62214
19.9%
60 61990
19.8%
50 224
 
0.1%

Most occurring characters

ValueCountFrequency (%)
249864
14.7%
e 189666
 
11.2%
i 126444
 
7.5%
l 126444
 
7.5%
n 125436
 
7.4%
t 125436
 
7.4%
f 63222
 
3.7%
a 63222
 
3.7%
r 63222
 
3.7%
s 63222
 
3.7%
Other values (9) 498720
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1195170
70.5%
Space Separator 249864
 
14.7%
Uppercase Letter 125436
 
7.4%
Decimal Number 124428
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 189666
15.9%
i 126444
10.6%
l 126444
10.6%
n 125436
10.5%
t 125436
10.5%
f 63222
 
5.3%
a 63222
 
5.3%
r 63222
 
5.3%
s 63222
 
5.3%
h 62214
 
5.2%
Other values (3) 186642
15.6%
Decimal Number
ValueCountFrequency (%)
0 62214
50.0%
6 61990
49.8%
5 224
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
B 63222
50.4%
H 62214
49.6%
Space Separator
ValueCountFrequency (%)
249864
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1320606
77.9%
Common 374292
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 189666
14.4%
i 126444
9.6%
l 126444
9.6%
n 125436
9.5%
t 125436
9.5%
f 63222
 
4.8%
a 63222
 
4.8%
r 63222
 
4.8%
s 63222
 
4.8%
B 63222
 
4.8%
Other values (5) 311070
23.6%
Common
ValueCountFrequency (%)
249864
66.8%
0 62214
 
16.6%
6 61990
 
16.6%
5 224
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1694898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
249864
14.7%
e 189666
 
11.2%
i 126444
 
7.5%
l 126444
 
7.5%
n 125436
 
7.4%
t 125436
 
7.4%
f 63222
 
3.7%
a 63222
 
3.7%
r 63222
 
3.7%
s 63222
 
3.7%
Other values (9) 498720
29.4%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size494.0 KiB
M
35661 
F
27561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters63222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 35661
56.4%
F 27561
43.6%

Length

2024-01-22T05:19:24.741902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T05:19:24.875041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
m 35661
56.4%
f 27561
43.6%

Most occurring characters

ValueCountFrequency (%)
M 35661
56.4%
F 27561
43.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 63222
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 35661
56.4%
F 27561
43.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 63222
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 35661
56.4%
F 27561
43.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 35661
56.4%
F 27561
43.6%

p_axis
Real number (ℝ)

Distinct338
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.569485
Minimum-179
Maximum180
Zeros291
Zeros (%)0.5%
Negative2733
Negative (%)4.3%
Memory size494.0 KiB
2024-01-22T05:19:25.320959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-179
5-th percentile1
Q136
median53
Q364
95-th percentile77
Maximum180
Range359
Interquartile range (IQR)28

Descriptive statistics

Standard deviation25.915806
Coefficient of variation (CV)0.54479897
Kurtosis7.4773633
Mean47.569485
Median Absolute Deviation (MAD)13
Skewness-1.6810751
Sum3007438
Variance671.62902
MonotonicityNot monotonic
2024-01-22T05:19:25.491636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 1659
 
2.6%
60 1642
 
2.6%
63 1593
 
2.5%
57 1561
 
2.5%
61 1541
 
2.4%
66 1490
 
2.4%
58 1476
 
2.3%
59 1473
 
2.3%
62 1396
 
2.2%
67 1299
 
2.1%
Other values (328) 48092
76.1%
ValueCountFrequency (%)
-179 2
< 0.1%
-178 2
< 0.1%
-177 2
< 0.1%
-176 1
< 0.1%
-175 2
< 0.1%
-172 1
< 0.1%
-171 1
< 0.1%
-169 1
< 0.1%
-167 1
< 0.1%
-165 1
< 0.1%
ValueCountFrequency (%)
180 3
< 0.1%
179 2
< 0.1%
177 4
< 0.1%
176 2
< 0.1%
173 1
 
< 0.1%
172 2
< 0.1%
171 1
 
< 0.1%
170 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%

p_end
Real number (ℝ)

Distinct87
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.63655
Minimum48
Maximum228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:25.655008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile118
Q1140
median150
Q3160
95-th percentile174
Maximum228
Range180
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.059174
Coefficient of variation (CV)0.11477106
Kurtosis1.1325095
Mean148.63655
Median Absolute Deviation (MAD)10
Skewness-0.59955363
Sum9397100
Variance291.01543
MonotonicityNot monotonic
2024-01-22T05:19:25.812876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 3408
 
5.4%
152 3394
 
5.4%
154 3392
 
5.4%
156 3338
 
5.3%
148 3174
 
5.0%
146 3157
 
5.0%
158 3056
 
4.8%
144 2800
 
4.4%
160 2740
 
4.3%
162 2540
 
4.0%
Other values (77) 32223
51.0%
ValueCountFrequency (%)
48 18
< 0.1%
50 7
 
< 0.1%
54 6
 
< 0.1%
56 4
 
< 0.1%
58 7
 
< 0.1%
60 5
 
< 0.1%
62 3
 
< 0.1%
64 1
 
< 0.1%
66 8
< 0.1%
68 5
 
< 0.1%
ValueCountFrequency (%)
228 1
 
< 0.1%
224 1
 
< 0.1%
220 1
 
< 0.1%
218 1
 
< 0.1%
216 3
< 0.1%
212 1
 
< 0.1%
210 1
 
< 0.1%
208 1
 
< 0.1%
206 1
 
< 0.1%
204 1
 
< 0.1%

p_onset
Real number (ℝ)

SKEWED 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.998197
Minimum8
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:25.963618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile40
Q140
median40
Q340
95-th percentile40
Maximum40
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20231785
Coefficient of variation (CV)0.0050581743
Kurtosis20670.266
Mean39.998197
Median Absolute Deviation (MAD)0
Skewness-138.09439
Sum2528766
Variance0.040932514
MonotonicityNot monotonic
2024-01-22T05:19:26.071946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
40 63214
> 99.9%
8 2
 
< 0.1%
34 2
 
< 0.1%
28 1
 
< 0.1%
32 1
 
< 0.1%
24 1
 
< 0.1%
38 1
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
24 1
 
< 0.1%
28 1
 
< 0.1%
32 1
 
< 0.1%
34 2
 
< 0.1%
38 1
 
< 0.1%
40 63214
> 99.9%
ValueCountFrequency (%)
40 63214
> 99.9%
38 1
 
< 0.1%
34 2
 
< 0.1%
32 1
 
< 0.1%
28 1
 
< 0.1%
24 1
 
< 0.1%
8 2
 
< 0.1%

qrs_axis
Real number (ℝ)

ZEROS 

Distinct254
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.028882
Minimum-178
Maximum177
Zeros1390
Zeros (%)2.2%
Negative13357
Negative (%)21.1%
Memory size494.0 KiB
2024-01-22T05:19:26.235320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-178
5-th percentile-16
Q12
median23
Q348
95-th percentile73
Maximum177
Range355
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.307739
Coefficient of variation (CV)1.1709568
Kurtosis0.0025912243
Mean25.028882
Median Absolute Deviation (MAD)23
Skewness0.030720164
Sum1582376
Variance858.94357
MonotonicityNot monotonic
2024-01-22T05:19:26.387810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1390
 
2.2%
13 1374
 
2.2%
3 1331
 
2.1%
-3 1251
 
2.0%
18 1188
 
1.9%
21 1105
 
1.7%
24 948
 
1.5%
-13 929
 
1.5%
26 915
 
1.4%
45 824
 
1.3%
Other values (244) 51967
82.2%
ValueCountFrequency (%)
-178 1
 
< 0.1%
-177 1
 
< 0.1%
-176 1
 
< 0.1%
-162 3
< 0.1%
-159 2
< 0.1%
-129 1
 
< 0.1%
-123 2
< 0.1%
-117 1
 
< 0.1%
-116 1
 
< 0.1%
-114 3
< 0.1%
ValueCountFrequency (%)
177 1
< 0.1%
175 1
< 0.1%
173 1
< 0.1%
171 1
< 0.1%
168 2
< 0.1%
167 1
< 0.1%
165 2
< 0.1%
160 1
< 0.1%
159 1
< 0.1%
158 1
< 0.1%

qrs_end
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.442
Minimum160
Maximum544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:26.553256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile246
Q1270
median290
Q3314
95-th percentile370
Maximum544
Range384
Interquartile range (IQR)44

Descriptive statistics

Standard deviation38.012425
Coefficient of variation (CV)0.12822888
Kurtosis2.0614825
Mean296.442
Median Absolute Deviation (MAD)22
Skewness1.1363528
Sum18741656
Variance1444.9444
MonotonicityNot monotonic
2024-01-22T05:19:26.706596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
284 1715
 
2.7%
276 1689
 
2.7%
282 1685
 
2.7%
278 1671
 
2.6%
290 1616
 
2.6%
274 1610
 
2.5%
286 1593
 
2.5%
280 1587
 
2.5%
288 1581
 
2.5%
294 1527
 
2.4%
Other values (166) 46948
74.3%
ValueCountFrequency (%)
160 1
 
< 0.1%
162 1
 
< 0.1%
164 1
 
< 0.1%
182 1
 
< 0.1%
184 1
 
< 0.1%
188 2
< 0.1%
192 1
 
< 0.1%
196 3
< 0.1%
200 1
 
< 0.1%
202 3
< 0.1%
ValueCountFrequency (%)
544 1
< 0.1%
538 1
< 0.1%
536 1
< 0.1%
534 1
< 0.1%
532 1
< 0.1%
530 1
< 0.1%
524 2
< 0.1%
522 1
< 0.1%
520 1
< 0.1%
518 1
< 0.1%

qrs_onset
Real number (ℝ)

HIGH CORRELATION 

Distinct155
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.404
Minimum70
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:26.933417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile162
Q1182
median198
Q3216
95-th percentile266
Maximum450
Range380
Interquartile range (IQR)34

Descriptive statistics

Standard deviation30.961334
Coefficient of variation (CV)0.15296799
Kurtosis2.8405735
Mean202.404
Median Absolute Deviation (MAD)18
Skewness1.2503022
Sum12796386
Variance958.60421
MonotonicityNot monotonic
2024-01-22T05:19:27.093970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196 2106
 
3.3%
194 2091
 
3.3%
192 1997
 
3.2%
188 1984
 
3.1%
190 1984
 
3.1%
186 1928
 
3.0%
198 1917
 
3.0%
200 1901
 
3.0%
184 1882
 
3.0%
202 1867
 
3.0%
Other values (145) 43565
68.9%
ValueCountFrequency (%)
70 1
 
< 0.1%
72 2
 
< 0.1%
80 1
 
< 0.1%
94 1
 
< 0.1%
96 1
 
< 0.1%
98 2
 
< 0.1%
100 2
 
< 0.1%
106 2
 
< 0.1%
110 6
< 0.1%
112 2
 
< 0.1%
ValueCountFrequency (%)
450 1
< 0.1%
424 1
< 0.1%
420 1
< 0.1%
410 1
< 0.1%
408 1
< 0.1%
402 1
< 0.1%
398 1
< 0.1%
394 2
< 0.1%
390 2
< 0.1%
384 1
< 0.1%

rr_interval
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean807.10673
Minimum295
Maximum2857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:27.237400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum295
5-th percentile594
Q1697
median800
Q3909
95-th percentile1052
Maximum2857
Range2562
Interquartile range (IQR)212

Descriptive statistics

Standard deviation152.31511
Coefficient of variation (CV)0.18871743
Kurtosis2.7023888
Mean807.10673
Median Absolute Deviation (MAD)103
Skewness0.77770622
Sum51026902
Variance23199.892
MonotonicityNot monotonic
2024-01-22T05:19:27.396941image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
909 1875
 
3.0%
869 1806
 
2.9%
800 1806
 
2.9%
845 1803
 
2.9%
882 1797
 
2.8%
833 1782
 
2.8%
857 1748
 
2.8%
789 1733
 
2.7%
821 1727
 
2.7%
895 1693
 
2.7%
Other values (124) 45452
71.9%
ValueCountFrequency (%)
295 1
< 0.1%
322 1
< 0.1%
326 1
< 0.1%
338 1
< 0.1%
365 1
< 0.1%
372 1
< 0.1%
375 1
< 0.1%
379 2
< 0.1%
394 1
< 0.1%
397 1
< 0.1%
ValueCountFrequency (%)
2857 1
 
< 0.1%
2222 1
 
< 0.1%
2142 2
 
< 0.1%
2068 2
 
< 0.1%
2000 2
 
< 0.1%
1935 1
 
< 0.1%
1875 2
 
< 0.1%
1818 5
< 0.1%
1764 4
< 0.1%
1714 6
< 0.1%

subject_id
Real number (ℝ)

Distinct21022
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15012177
Minimum10001217
Maximum19999987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:27.545401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum10001217
5-th percentile10507925
Q112544332
median14993494
Q317517983
95-th percentile19500621
Maximum19999987
Range9998770
Interquartile range (IQR)4973651

Descriptive statistics

Standard deviation2878760.9
Coefficient of variation (CV)0.19176172
Kurtosis-1.1967185
Mean15012177
Median Absolute Deviation (MAD)2487729
Skewness0.009403189
Sum9.4909987 × 1011
Variance8.2872644 × 1012
MonotonicityIncreasing
2024-01-22T05:19:27.714821image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18932584 116
 
0.2%
13398773 87
 
0.1%
11888614 69
 
0.1%
18136887 60
 
0.1%
16602058 58
 
0.1%
15145407 51
 
0.1%
18902344 50
 
0.1%
13050816 49
 
0.1%
10965697 48
 
0.1%
12298456 46
 
0.1%
Other values (21012) 62588
99.0%
ValueCountFrequency (%)
10001217 1
 
< 0.1%
10001884 4
 
< 0.1%
10002013 10
< 0.1%
10002428 2
 
< 0.1%
10002930 3
 
< 0.1%
10003019 6
< 0.1%
10004235 2
 
< 0.1%
10004422 1
 
< 0.1%
10004457 8
< 0.1%
10004720 3
 
< 0.1%
ValueCountFrequency (%)
19999987 2
 
< 0.1%
19999840 1
 
< 0.1%
19999442 4
 
< 0.1%
19999287 2
 
< 0.1%
19999068 1
 
< 0.1%
19998770 3
 
< 0.1%
19997448 10
< 0.1%
19995790 8
< 0.1%
19995780 2
 
< 0.1%
19995258 2
 
< 0.1%

t_axis
Real number (ℝ)

Distinct361
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.946111
Minimum-180
Maximum180
Zeros138
Zeros (%)0.2%
Negative4774
Negative (%)7.6%
Memory size494.0 KiB
2024-01-22T05:19:27.889256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-31
Q124
median44
Q362
95-th percentile126
Maximum180
Range360
Interquartile range (IQR)38

Descriptive statistics

Standard deviation49.52158
Coefficient of variation (CV)1.1806
Kurtosis5.3106543
Mean41.946111
Median Absolute Deviation (MAD)19
Skewness-1.1763041
Sum2651917
Variance2452.3869
MonotonicityNot monotonic
2024-01-22T05:19:28.025051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 1236
 
2.0%
50 1107
 
1.8%
48 1105
 
1.7%
38 1043
 
1.6%
56 1022
 
1.6%
39 1018
 
1.6%
37 1007
 
1.6%
41 999
 
1.6%
42 986
 
1.6%
43 984
 
1.6%
Other values (351) 52715
83.4%
ValueCountFrequency (%)
-180 34
0.1%
-179 28
 
< 0.1%
-178 33
0.1%
-177 76
0.1%
-176 26
 
< 0.1%
-175 36
0.1%
-174 25
 
< 0.1%
-173 33
0.1%
-172 35
0.1%
-171 32
0.1%
ValueCountFrequency (%)
180 37
0.1%
179 41
0.1%
178 40
0.1%
177 61
0.1%
176 43
0.1%
175 41
0.1%
174 46
0.1%
173 41
0.1%
172 36
0.1%
171 38
0.1%

t_end
Real number (ℝ)

HIGH CORRELATION 

Distinct234
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean594.43754
Minimum390
Maximum1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size494.0 KiB
2024-01-22T05:19:28.172750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile514
Q1558
median590
Q3626
95-th percentile692
Maximum1024
Range634
Interquartile range (IQR)68

Descriptive statistics

Standard deviation54.423706
Coefficient of variation (CV)0.091554962
Kurtosis1.073274
Mean594.43754
Median Absolute Deviation (MAD)34
Skewness0.58879099
Sum37581530
Variance2961.9398
MonotonicityNot monotonic
2024-01-22T05:19:28.325158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
578 1082
 
1.7%
582 1058
 
1.7%
592 1045
 
1.7%
596 1036
 
1.6%
588 1032
 
1.6%
586 1031
 
1.6%
590 1016
 
1.6%
598 1008
 
1.6%
568 1005
 
1.6%
580 988
 
1.6%
Other values (224) 52921
83.7%
ValueCountFrequency (%)
390 2
 
< 0.1%
400 2
 
< 0.1%
406 1
 
< 0.1%
410 1
 
< 0.1%
416 1
 
< 0.1%
418 2
 
< 0.1%
422 2
 
< 0.1%
426 4
< 0.1%
428 4
< 0.1%
430 5
< 0.1%
ValueCountFrequency (%)
1024 1
< 0.1%
928 1
< 0.1%
918 1
< 0.1%
908 1
< 0.1%
892 2
< 0.1%
884 1
< 0.1%
882 2
< 0.1%
874 1
< 0.1%
868 2
< 0.1%
866 1
< 0.1%

target_variable
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size494.0 KiB
Normal ECG
34522 
Abnormal ECG
28700 

Length

Max length12
Median length10
Mean length10.907912
Min length10

Characters and Unicode

Total characters689620
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal ECG
2nd rowAbnormal ECG
3rd rowAbnormal ECG
4th rowAbnormal ECG
5th rowAbnormal ECG

Common Values

ValueCountFrequency (%)
Normal ECG 34522
54.6%
Abnormal ECG 28700
45.4%

Length

2024-01-22T05:19:28.500309image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T05:19:28.630795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
ecg 63222
50.0%
normal 34522
27.3%
abnormal 28700
22.7%

Most occurring characters

ValueCountFrequency (%)
o 63222
9.2%
r 63222
9.2%
m 63222
9.2%
a 63222
9.2%
l 63222
9.2%
63222
9.2%
E 63222
9.2%
C 63222
9.2%
G 63222
9.2%
N 34522
5.0%
Other values (3) 86100
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 373510
54.2%
Uppercase Letter 252888
36.7%
Space Separator 63222
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 63222
16.9%
r 63222
16.9%
m 63222
16.9%
a 63222
16.9%
l 63222
16.9%
b 28700
7.7%
n 28700
7.7%
Uppercase Letter
ValueCountFrequency (%)
E 63222
25.0%
C 63222
25.0%
G 63222
25.0%
N 34522
13.7%
A 28700
11.3%
Space Separator
ValueCountFrequency (%)
63222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 626398
90.8%
Common 63222
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 63222
10.1%
r 63222
10.1%
m 63222
10.1%
a 63222
10.1%
l 63222
10.1%
E 63222
10.1%
C 63222
10.1%
G 63222
10.1%
N 34522
5.5%
A 28700
4.6%
Other values (2) 57400
9.2%
Common
ValueCountFrequency (%)
63222
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 689620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 63222
9.2%
r 63222
9.2%
m 63222
9.2%
a 63222
9.2%
l 63222
9.2%
63222
9.2%
E 63222
9.2%
C 63222
9.2%
G 63222
9.2%
N 34522
5.0%
Other values (3) 86100
12.5%

Interactions

2024-01-22T05:19:21.433042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:04.753221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:06.388877image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:07.998469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:09.756301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:11.395793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:12.941231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:14.628692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:16.302772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:18.139530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:19.761575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:21.587399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:04.942148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:06.524172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:08.160253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:09.921749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:11.533025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:13.146551image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:14.778028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:16.735219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:18.281075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:19.897614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:21.724417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:05.080900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:06.664577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:08.303932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:10.059288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:11.674655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:13.307783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:14.942702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:16.882729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:18.434988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:20.032838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:21.877970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:05.241351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:06.810593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:08.456854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:10.213783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:11.833572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:13.466760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:15.082153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.024254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:18.594280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:20.193101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:22.021249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:05.405060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:06.939367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:08.590668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:10.345718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:11.964234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:13.614634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:15.229717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.156811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:18.739603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:20.337705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:22.158169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:05.544632image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:07.097786image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:08.722028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:10.489734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:12.101691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:13.749536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:15.356609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.283409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:18.892214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:20.500152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:22.304356image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:05.707929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:07.240349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:08.864982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:10.691267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:12.237276image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:13.894084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:15.520181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.428905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:19.086272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:20.670604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:22.436280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:05.863344image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:07.384843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:09.010497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:10.830413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:12.385106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:14.046018image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:15.685039image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.579401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:19.245829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:20.844993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:22.570572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:05.992358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:07.524946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:09.151361image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:10.952015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:12.507832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:14.189665image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:15.821583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.706974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:19.364893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:20.977557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:22.719063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:06.126541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:07.688011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:09.291874image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:11.093077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:12.636302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:14.340603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:15.996629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.830558image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:19.492461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:21.151982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:22.876923image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:06.256817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:07.845457image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:09.422040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:11.229471image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:12.803148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:14.480136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:16.168139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:17.972088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:19.622985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-22T05:19:21.301477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2024-01-22T05:19:28.754939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
anchor_agebandwidthfilteringgenderp_axisp_endp_onsetqrs_axisqrs_endqrs_onsetrr_intervalsubject_idt_axist_endtarget_variable
anchor_age1.0000.0140.0150.1400.0260.092-0.013-0.2260.2580.2250.186-0.0040.0880.2840.255
bandwidth0.0141.0000.0330.022-0.003-0.047-0.008-0.016-0.046-0.0360.001-0.0030.009-0.0450.073
filtering0.0150.0331.0000.0110.0010.0090.001-0.001-0.004-0.003-0.0150.004-0.009-0.0160.008
gender0.1400.0220.0111.000-0.0850.0770.001-0.0190.1570.0920.0570.012-0.0450.0200.048
p_axis0.026-0.0030.001-0.0851.000-0.006-0.0060.260-0.060-0.046-0.1420.0230.258-0.0940.142
p_end0.092-0.0470.0090.077-0.0061.0000.002-0.0770.4260.4580.142-0.007-0.0140.3300.149
p_onset-0.013-0.0080.0010.001-0.0060.0021.0000.002-0.019-0.019-0.0110.0020.004-0.0190.007
qrs_axis-0.226-0.016-0.001-0.0190.260-0.0770.0021.000-0.160-0.139-0.0740.0100.084-0.1490.189
qrs_end0.258-0.046-0.0040.157-0.0600.426-0.019-0.1601.0000.8720.284-0.0040.0310.7230.416
qrs_onset0.225-0.036-0.0030.092-0.0460.458-0.019-0.1390.8721.0000.269-0.0040.0300.6950.312
rr_interval0.1860.001-0.0150.057-0.1420.142-0.011-0.0740.2840.2691.000-0.009-0.0210.6900.294
subject_id-0.004-0.0030.0040.0120.023-0.0070.0020.010-0.004-0.004-0.0091.000-0.004-0.0060.029
t_axis0.0880.009-0.009-0.0450.258-0.0140.0040.0840.0310.030-0.021-0.0041.0000.0360.488
t_end0.284-0.045-0.0160.020-0.0940.330-0.019-0.1490.7230.6950.690-0.0060.0361.0000.365
target_variable0.2550.0730.0080.0480.1420.1490.0070.1890.4160.3120.2940.0290.4880.3651.000

Missing values

2024-01-22T05:19:23.083463image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-22T05:19:23.426373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

anchor_agebandwidthfilteringgenderp_axisp_endp_onsetqrs_axisqrs_endqrs_onsetrr_intervalsubject_idt_axist_endtarget_variable
0550.005-150 Hz60 Hz notch Baseline filterF6715640152982048951000121748622Normal ECG
1680.005-150 Hz60 Hz notch Baseline filterF4815440-1932818888210001884132626Abnormal ECG
2680.005-150 Hz60 Hz notch Baseline filterF8011840-1828815269710001884119546Abnormal ECG
3680.005-150 Hz60 Hz notch Baseline filterF-8711240-2229215288210001884126590Abnormal ECG
4680.005-150 Hz60 Hz notch Baseline filterF7114440-1332218092310001884117614Abnormal ECG
5530.005-150 Hz60 Hz notch Baseline filterF33150401632824285710002013115690Abnormal ECG
6530.005-150 Hz60 Hz notch Baseline filterF5515840173182307221000201365634Normal ECG
7530.005-150 Hz60 Hz notch Baseline filterF5715640403322387501000201382658Normal ECG
8530.005-150 Hz60 Hz notch Baseline filterF59118403530221668110002013105628Abnormal ECG
9530.005-150 Hz60 Hz notch Baseline filterF72146403131422856610002013-133558Abnormal ECG
anchor_agebandwidthfilteringgenderp_axisp_endp_onsetqrs_axisqrs_endqrs_onsetrr_intervalsubject_idt_axist_endtarget_variable
63212630.005-150 Hz60 Hz notch Baseline filterM301424012541747891999906834580Normal ECG
63213710.005-150 Hz60 Hz notch Baseline filterF4715640613442627501999928752630Abnormal ECG
63214710.005-150 Hz60 Hz notch Baseline filterF3715440503422726741999928781604Abnormal ECG
63215410.005-150 Hz60 Hz notch Baseline filterM6716040522942128951999944250584Normal ECG
63216410.005-150 Hz60 Hz notch Baseline filterM76168403228620073119999442-44582Abnormal ECG
63217410.005-150 Hz60 Hz notch Baseline filterM7017640563122189091999944236618Normal ECG
63218410.005-150 Hz60 Hz notch Baseline filterM7616640612962128451999944257594Normal ECG
63219580.05-150 Hz60 Hz notch Baseline filterM4914040-292881946451999984051502Normal ECG
63220570.005-150 Hz60 Hz notch Baseline filterF5915640542822026311999998756570Normal ECG
63221570.005-150 Hz60 Hz notch Baseline filterF5515640562842066891999998752594Abnormal ECG